Graduate School of Biomedical Sciences, Program in Quantitative and Computational Biosciences, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, United States.
Lester and Sue Smith Breast Center, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, United States.
Bioinformatics. 2023 Jul 1;39(7). doi: 10.1093/bioinformatics/btad431.
In the era where transcriptome profiling moves toward single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here, we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics datasets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing genes' spatial correlations and cell types' colocalization within the precise spatial context. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations.
The Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/. The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL.
在转录组分析向单细胞和空间分辨率发展的时代，传统的共表达分析缺乏充分利用这些丰富信息来揭示空间基因关联的能力。在这里，我们介绍了一个名为使用 L-index 进行基因关联的空间富集分析的 Python 包（Spatial Enrichment Analysis of Gene Associations using L-index，SEAGAL），用于在单细胞和基因集水平上检测和可视化空间基因相关性。我们的包以具有基因表达和对齐空间坐标的空间转录组数据集作为输入。它允许在精确的空间背景下分析和可视化基因的空间相关性和细胞类型的共定位。输出可以通过几行代码可视化作为火山图和热图，从而为挖掘空间基因关联提供了一个简单而全面的工具。
可以使用 pip 通过以下网址安装 Python 包 SEAGAL：https://pypi.org/project/seagal/。源代码和逐步教程可在以下网址获得：https://github.com/linhuawang/SEAGAL。
